Chao Wu

Bio: Chao Wu is an academic researcher from Shenzhen University. The author has contributed to research in topics: Switched reluctance motor & Traffic flow. The author has an hindex of 5, co-authored 17 publications receiving 93 citations. Previous affiliations of Chao Wu include Central South University.

Robust Planning of Electric Vehicle Charging Facilities With an Advanced Evaluation Method

Abstract: The planning of charging facilities (CFs) for electric vehicles (EVs) plays an important role for the extensive applications of EVs. Uncertainties existing in the development of future EV technology should be properly modeled to ensure the robustness of the planning scheme. The uncertainties concerned include EV development types, growth rate of load and traffic flow, and distributions of load and traffic flow in the smart grid. The existing single-stage planning model cannot fully evaluate the risks brought by all kinds of uncertainties. Given this background, the multistage CF planning problem considering uncertainties is studied in this work. First, several typical uncertainties in future smart grid with a high penetration of EVs are considered to generate development scenarios for multistage planning. Then, the well-established data envelopment analysis is utilized to evaluate the planning schemes while the novel EV expected energy not supplied cost is defined to measure the service ability of CFs. The final planning result obtained by the proposed framework will not only have good performance in the current stage but also exhibit robustness for all the considered scenarios in the future stage with respect to uncertainties. The application potential of the designed multistage planning framework is proved by an example with both the distribution network and traffic network included.

Maximum-Force-per-Ampere Strategy of Current Distribution for Efficiency Improvement in Planar Switched Reluctance Motors

Abstract: This paper proposes a novel maximum-force-per-ampere strategy for the current distribution of planar switched reluctance motors (PSRMs) for efficiency improvement. This strategy is the first of its kind for planar motors, and it is used to generate the desired thrust force with the minimum sum of squares of the three-phase current. To formulate this strategy, a constrained optimization problem with time-varying parameters is first developed. Then, the problem is transformed into an unconstrained problem with a barrier function. Additionally, a self-designed adaptive genetic algorithm is introduced to solve the unconstrained optimization problem for locating the optimal currents. Comparative studies of the proposed and conventional strategies for a PSRM system are carried out via simulation and experiment, and planar trajectory tracking for the system with the proposed strategy is experimentally performed. The validity of the proposed strategy is also verified.

Predictive Position Control of Long-Stroke Planar Motors for High-Precision Positioning Applications

Abstract: This article proposes a predictive position control of long-stroke planar motors to achieve micrometer-scale positioning under long-stroke time-varying trajectory tracking for use in high-precision positioning applications. The motivation consists in the potential application of model predictive control in long-stroke planar motors for high-precision positioning systems. This control is the first of its kind for planar motors. A dynamic model of a long-stroke planar motor developed in the laboratory is built, and then a predictive model is established to predict future positions of the motor. An additional term is introduced to a cost function to improve the positioning accuracy, which provides an output feedback control action to the motor for reducing the model error of the predictive model. By minimizing the cost function, an analytically explicit solution of the optimal control sequence is obtained, which makes the computational burden of the controller low. The stability of the control system is discussed based on the closed-loop state-space model. Moreover, the selection of stable control parameters is theoretically given. Simulation and experimental results demonstrate the effectiveness of the proposed control method of long-stroke planar motors for use in high-precision positioning applications.

Predictive Position Control of Planar Motors Using Trajectory Gradient Soft Constraint With Attenuation Coefficients in the Weighting Matrix

Abstract: This article proposes a predictive position control method of planar motors using trajectory gradient soft constraint with attenuation coefficients in the weighting matrix to achieve high-precision, time-varying, and long-stroke positioning Based on a built dynamics model of a planar motor developed in the laboratory, a predictive model is established to predict the future positions of the motor To improve the positioning precision, a soft constraint defined by a trajectory gradient difference between the gradients of the reference position and predictive position sequences is introduced to the cost function Then, an explicitly analytical solution of the optimal control is obtained by minimizing the cost function To highlight the stronger effects of the trajectory gradients closer to the current time, the attenuation coefficients are applied to the weighting matrix of the added soft constraint The stability of the control system is proved employing the linear quadratic optimal control method and the Lyapunov stability theory Moreover, the time complexity is discussed based on the analytical control action to show low computational burden of the proposed method Finally, the given simulation and experimental results demonstrate the effectiveness of the proposed method to achieve high-precision time-varying positioning for planar motors

Spatiotemporal Scenario Generation of Traffic Flow Based on LSTM-GAN

Abstract: In recent years, a surging development of vehicles and continuous enhancement of transportation infrastructures have been witnessed worldwide, leading to a remarkable growing of traffic flow data. The traffic data is highly valuable in today’s society, accurate modelling of traffic flow for the concerned areas can significantly benefit the government agencies, related commercial departments and individuals. Specifically, road users are allowed to make better traveling decisions, avoid traffic congestion, reduce carbon emissions and improve traffic operation efficiency. In order to estimate the possible traffic flow scenarios within a specific area for multiple horizons, we propose a scenario generation model based on sequential generative adversarial networks (LSTM-GAN) where the long short term memory (LSTM) network is incorporated to capture the temporal dynamics involved in traffic flows. Through game training, the spatiotemporal scenarios of traffic flow in line with the characteristics of observed road network traffic flow can be well generated. These traffic scenarios can be applied in the design and planning of road traffic system, as well as in the virtual training cases of intelligent driving.

Routing and Scheduling of Mobile Power Sources for Distribution System Resilience Enhancement

Abstract: Mobile power sources (MPSs), including electric vehicle fleets, truck-mounted mobile energy storage systems, and mobile emergency generators, have great potential to enhance distribution system (DS) resilience against extreme weather events. However, their dispatch is not well investigated. This paper implements resilient routing and scheduling of MPSs via a two-stage framework. In the first stage, i.e., before the event, MPSs are pre-positioned in the DS to enable rapid pre-restoration, in order to enhance survivability of the electricity supply to critical loads. DS network is also proactively reconfigured into a less impacted or stressed state. A two-stage robust optimization model is constructed and solved by the column-and-constraint generation algorithm to derive first-stage decisions. In the second stage, i.e., after the event, MPSs are dynamically dispatched in the DS to coordinate with conventional restoration efforts, so as to enhance system recovery. A novel mixed-integer programming model that resolves different timescales of MPS dispatch and DS operation, coupling of road and power networks, etc., is formulated to optimize dynamic dispatch of MPSs. Case studies conducted on IEEE 33-node and 123-node test systems demonstrate the proposed method’s effectiveness in routing and scheduling MPSs for DS resilience enhancement.

Book Review: Adaptive Optimal Control: the thinking man's GPCAdaptive Optimal Control: the thinking man's GPC:BITMEADR. R., GEVERSM. and WERTZV. (Prentice-Hall, 1990, 244 pp., £32.95)

Abstract: Finite Dill'erenzlOsung von elektrodynamiscben Problemen Die Einfiihrung der finiten Differenzmethode in Studentenkursen war bisher auf einfache ProbIerne beschriinkt, die so gut wie nichts mit der Welt, in der Studenten leben, zu tun hatten. In diesem Artikel stellen wir in einfacher Form einige nichttriviale Probleme aus dem Alltag vor, wie sie im Unterricht an der Temple University behandelt wurden.

Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach

Abstract: The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 min. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.